Human Activity Recognition for Production and Logistics-A Systematic Literature Review

被引：39

作者：

Reining, Christopher ^{[1
]}

Niemann, Friedrich ^{[1
]}

Rueda, Fernando Moya ^{[2
]}

Fink, Gernot A. ^{[2
]}

ten Hompel, Michael ^{[1
]}

机构：

[1] TU Dortmund Univ, Chair Mat Handling & Warehousing, Joseph von Fraunhofer Str 2-4, D-44227 Dortmund, Germany

[2] TU Dortmund Univ, Pattern Recognit Embedded Syst Grp, Otto Hahn Str 16, D-44227 Dortmund, Germany

来源：

INFORMATION | 2019年 / 10卷 / 08期

关键词：

Human Activity Recognition; production; Logistics; Motion Capturing; Inertial Measurement Unit; accelerometer; deep learning; statistical pattern recognition; ACCELEROMETER; FUTURE; SMARTPHONE; CHALLENGES; ALGORITHM; EXERCISE; FEATURES; MODELS;

D O I：

10.3390/info10080245

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner's perspective.

引用

页数：28

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